Method and device for controlling a technical system using a control model

ABSTRACT

In order to control a technical system using a control model, a transformation function is provided for reducing and/or obfuscating operating data of the technical system so as to obtain transformed operating data. In addition, the control model is generated by a model generator according to a first set of operating data of the technical system. In an access domain separated from the control model, a second set of operating data of the technical system is recorded and transformed by the transformation function into a transformed second set of operating data which is received by a model execution system. The control model is then executed by the model execution system, by supplying the transformed second set of operating data in an access domain separated from the second set of operating data, control data being derived from the transformed second set of operating data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to PCT Application No.PCT/EP2017/068755, having a filing date of Jul. 25, 2017, which is basedon European Application No. 16186109.1, having a filing date of Aug. 29,2016, the entire contents both of which are hereby incorporated byreference.

FIELD OF TECHNOLOGY

The following relates to a method and device for controlling a technicalsystem using a control model.

BACKGROUND

As a rule, the optimization of a behavior, an effect and/or a yield of atechnical system in respect of predetermined criteria, in particular, isdesirable when controlling complex technical systems, such as, e.g., gasturbines, wind turbines, manufacturing lines, motor vehicles or medicalor technical imaging or analysis systems. To this end, contemporarycontrollers often use complex control models which derive specificcontrol data for the purposes of controlling the technical system fromoperating data of said technical system. Such control models can beused, in particular, for simulating, predicting, analyzing and/orclassifying operating data of the technical system. Contemporary controlmodels are often based on simulation techniques or techniques frommachine learning, e.g., by means of neural networks, and can bespecifically trained or designed to optimize the control in respect ofpredetermined criteria on the basis of training data or other data ofthe technical system.

As a rule, creating and implementing a control model that is optimizedfor a technical system requires specific knowledge and a significantdevelopment outlay. Therefore such control models are often designed bycorrespondingly specialized vendors. However, as a rule, the vendorrequires data of the technical system for the purposes of developing ortraining such a control model. Data of the technical system shouldlikewise be supplied to the control model when the designed controlmodel is used to control the technical system at a later stage. However,it is often in the interest of a user or operator of the technicalsystem that critical data of the technical system is not provided toexternal parties. Conversely, a corresponding interest of the controlmodel vendor is that implementation details of their control model arenot provided to external parties.

Nevertheless, as a rule, the control model and the data of the technicalsystem must, as it were, come together at a point to design or carry outthe control model. Consequently, a problem arising is that of being ableto ensure details of the internal workings of the control model on theone hand and of critical data of the technical system on the other handare kept confidential from the respective other party.

One option for maintaining a certain amount of confidentiality consistsof the model vendor providing their control model to the user of thetechnical system only in encrypted form. In order to carry out theencrypted control model at the user, the latter is moreover providedwith an interpreter which can at least temporarily decrypt the controlmodel at run time. Provided that, on the one hand, the control model iscarried out at the user, their data remain confidential as a rule.Provided that, on the other hand, the control model is encrypted, theuser is not able to directly access the internal workings of the model;however, the user could decompile the interpreter and thus compromisethe encryption of the control model. Although such decompiling isconnected with significant outlay as a rule, the confidentiality of theinner workings of the model falls with the willingness of the user toinvest therein.

An aspect relates to specify a method and an arrangement for controllinga technical system based on a control model, in which confidentiality ofthe control model and of data of the technical system is better ensured.

SUMMARY

A transformation function for reducing and/or obfuscating operating dataof the technical system to form transformed operating data is providedfor the purposes of controlling a technical system based on a controlmodel. Here, obfuscation should be understood to mean, in particular, aconcealment of data, e.g., by encoding, dicing, hiding and/orrearranging the latter, such that a reconstruction of the originaloperating data without a priori knowledge is made at least substantiallymore difficult. The control model is generated by a model generator as afunction of first operating data of the technical system. According toembodiments of the invention, second operating data of the technicalsystem are captured in an access domain that is separated from thecontrol model and said second operating data are transformed intotransformed second operating data by the transformation function; saidtransformed second operating data are received by a model executionsystem. The control model is executed by the model execution system witha supply of the transformed second operating data in an access domainthat is separated from the second operating data, with control databeing derived from the transformed second operating data. The controldata are transmitted for the purposes of controlling the technicalsystem.

Here, in particular, an access domain should be understood to mean adomain within a data network whose data objects can be accessed fromwithin the domain. Accordingly, data access to a data object that isseparated from an access domain from said access domain is not possible,or data access is at least made substantially more difficult.

An arrangement, a computer program product (non-transitory computerreadable storage medium having instructions, which when executed by aprocessor, perform actions) and a computer-readable storage medium areprovided to carry out the method according to embodiments of theinvention. The arrangement for carrying out the method according toembodiments of the invention can be implemented, in particular, by meansof data processors such as, e.g., by means of ASICs(application-specific integrated circuits), DSPs (digital signalprocessors) and/or FPGAs (field-programmable gate arrays).

One advantage of embodiments of the invention should be considered thatof rendering access to the control model by an operator of the technicalsystem on the one hand and rendering access to operating data of thetechnical system by a model vendor on the other hand at leastsubstantially more difficult in a simple manner. Here, there is no needto encrypt the control model.

According to an advantageous embodiment of the invention, the controlmodel can be generated and, in particular, trained on the basis of firstoperating data that were transformed by the transformation function.This can prevent original operating data of the technical system beingmade available to a model vendor, for example for training purposes.Since, in many cases, a training success is not substantially impairedby a preceding transformation of training data, the control model, as arule, can also be effectively trained based on transformed operatingdata.

Advantageously, the control model can comprise a neural network, adata-driven regressor, a support vector machine and/or a decision tree.A multiplicity of efficient training and learning methods are availablefor the aforementioned implementation variants of the control model.

Advantageously, the model generator and/or the model execution systemcan be operated by a model vendor in a manner separated from thetechnical system and, in particular, in an access domain that isseparated from the technical system.

According to an advantageous embodiment of the invention, thetransformation function can be provided by the model vendor and cancarry out information reduction. This is advantageous to the extent thatthe transformation function and the control model can be matchedparticularly well to one another by the model vendor. The informationreduction in this case can make it substantially more difficult for themodel vendor to deduce the original operating data of the technicalsystem.

According to a further advantageous embodiment of the invention, thetransformation function can be provided by an operator of the technicalsystem and can carry out information obfuscation and/or informationreduction. This is advantageous to the extent that the implementation ofthe transformation function can be taken away from the model vendor.This can better ensure the confidentiality of the operating data fromthe model vendor.

According to a further advantageous embodiment of the invention, aninitial model can be trained based on the first operating data and saidinitial model can be split into a first partial function and a secondpartial function. Then, the first partial function can be provided astransformation function and the second partial function can be providedas control model. This allows a trained transformation function and atrained control model to be provided in particularly simple fashion. Thepartial functions which, as it were, were trained together can bematched particularly well to one another in the process. In oneimplementation of the initial model as a neural network with at leastone hidden layer, this neural network can be split in a hidden layerinto two partial neural networks, which then respectively implement thefirst and the second partial function. The first partial function maycomprise an original input layer as an input layer and the hidden layeras an output layer. The second partial function may comprise the hiddenlayer as an input layer and the original output layer as an outputlayer.

According to a particularly advantageous embodiment of the invention,the transformation function can comprise a neural autoencoder. A neuralautoencoder allows an efficient and, as a rule, non-user-interpretablecoding and/or reduction of the operating data while largely maintainingessential data content.

Moreover, the transformation function can comprise multiplication by arandom matrix. Here, the random matrix can be populated by randomvalues. An invertible random matrix can be provided for the purposes ofobfuscating the operating data. For the purposes of reducing theoperating data, provision can be made of a non-invertible random matrix,in particular a non-square random matrix.

Moreover, the control model can be trained based on data that areindependent of the technical system, e.g., on the basis of time data,date specifications, weather data and/or other environmental data. Inthis way, it is also possible to take account of external influencingdata, which influence a behavior of the technical system, whenoptimizing the control.

BRIEF DESCRIPTION

Some of the embodiments will be described in detail, with reference tothe following figures, wherein like designations denote like members,wherein:

FIG. 1A elucidates training of a control model according to a firstexemplary embodiment;

FIG. 1B elucidates training of a control model according to a secondexemplary embodiment;

FIG. 2 elucidates a neural autoencoder as a transformation function;

FIG. 3 elucidates an implementation of a trained control model forcontrolling a technical system; and

FIG. 4 elucidates training of an initial model and the split thereofinto a transformation function and a control model.

DETAILED DESCRIPTION

FIGS. 1A and 1B elucidate a control model being generated or trainedaccording to various exemplary embodiments. In FIGS. 1A and 1B,equivalent entities are denoted by the same reference signs.

FIGS. 1A and 1B each show a technical system TS, e.g., a power plant, aturbine, a production line, a motor vehicle or a medical or technicalimaging or analysis system, in a schematic representation. A controllerCTL is coupled to the technical system TS for the purposes ofcontrolling the technical system TS. The controller CTL can beimplemented as part of the technical system TS or can be implementedwholly or partly external to the technical system TS. Furthermore, FIGS.1A and 1B show a model generator MG for generating and training acontrol model, and a model execution system MES with an interpreter INTfor carrying out the control model, said model execution system beingcoupled to the model generator MG.

The technical system TS comprises sensors S for capturing operating dataof the technical system TS. By way of example, such operating data canbe physical, control-related and/or construction-dependent operatingvariables, properties, predetermined values, state data, system data,control data, sensor data, image data, such as, e.g., x-ray images,measured values, ambient data, or other data arising during theoperation of the technical system TS. The operating data of thetechnical system TS are represented by suitable data structures, inparticular by higher-dimensional vectors.

An operator BTS of the technical system TS operates, uses and/orcontrols the technical system TS and the controller CTL. By contrast,the model generator MG and the model execution system MES are operatedby a model vendor MA, who produces a control model for controlling atechnical system TS.

The operator BTS of the technical system TS has data access to a firstaccess domain AC1, which is separated from a second access domain AC2 ofthe model vendor MA. This means that the operator BTS has no data accessto the second access domain AC2. Accordingly, the model vendor MA hasdata access to the second access domain AC2 but no data access to thefirst access domain AC1. In FIGS. 1A and 1B, the first access domain AC1and the second access domain AC2 are separated by a dashed line.

The technical system TS and the controller CTL are situated in the firstaccess domain AC1 and accordingly have no data access to the secondaccess domain AC2. The model generator MG and the model execution systemMES are situated in the second access domain AC2 and accordingly have nodata access to the first access domain AC1.

In particular, the model generator MG serves to train a control modelfor the technical system TS. Here, training should be understood tomean, in general, a mapping of input parameters of a model, e.g., of aneural network, on one or more target variables. This mapping isoptimized during a training phase of the model according topredeterminable or learned criteria, or according to criteria to belearned. In particular, a performance, a resource consumption, a yieldand/or a wear of the technical system and/or a production quality, aprediction quality, a classification quality, an analysis quality and/ora simulation quality can be used as criteria. Such training should beunderstood to mean, in particular, training of a neural network, adata-driven regression, parameter fitting for an analytical model or anyother model optimization method. In the case of a neural network,training optimizes, e.g., a network structure of neurons, thresholds ofneurons and/or weightings of edges between neurons in respect of anoptimization criterion. Coefficients of an employed regressor model canbe optimized when training a regressor.

As an alternative or in addition thereto, the model generator MG and/orthe model execution system MES can be implemented at least partlyoutside of the second access domain AC2, e.g., in a cloud, providedthere is no data access to the first access domain AC1.

The controller CTL, the model generator MG and the model executionsystem MES each comprise one or more processors for carrying out allmethod steps of the controller CTL, the model generator MG and the modelexecution system MES, respectively, and each comprise one or morememories for storing all data to be processed by the controller CTL, themodel generator MG and the model execution system MES, respectively.

In the exemplary embodiments elucidated by FIGS. 1A and 1B, firstoperating data BD1 of the technical system TS are captured, e.g., bymeans of the sensors S, and transmitted from the technical system TS tothe controller CTL. The first operating data BD1 are used as trainingdata for generating and, in particular, training the control model.

Specifically, FIG. 1A elucidates a generation or training of a controlmodel H in the case where the operator BTS is prepared to provide themodel vendor MA with operating data of the technical system TS, thetraining data BD1 in this case, for the purposes of training the controlmodel H. In this case, the first operating data BD1 are transmitted fromthe controller CTL to the model generator MG, i.e., from the firstaccess domain AC1 into the second access domain AC2. Based on thetransmitted first operating data BD1, the model generator MG generatesand trains a transformation function G and the control model H in thesecond access domain AC2, i.e., outside of the first access domain AC1.

The transformation function G serves to obfuscate and/or reduceoperating data of the technical system TS to form transformed operatingdata. The intention is that the operating data are transformed by thetransformation function G in such a way that access to, orreconstruction of, the original operating data is made substantiallymore difficult. In particular, the transformed operating data should notbe user-interpretable. As a result of the obfuscation, i.e.,concealment, input data, i.e., the first operating data BD1 in thiscase, are converted, in particular encoded, hidden, diced and/orrearranged, in such a way that a reconstruction of the input databecomes substantially more difficult without a priori knowledge.Although information content of the input data may be maintained in thiscase, it is only maintained in a form that is not readily interpretableor reconstructable. As an alternative or in addition thereto,information that is less relevant to the controller of the technicalsystem TS should be removed from the first operating data BD1 by thereduction of the operating data BD1 and, where possible, onlycontroller-relevant information should be maintained. As a result ofsuch an information reduction, an information content of the firstoperating data BD1, and hence, in particular, a dimension of therepresenting operating data vectors, can be reduced without substantialloss of controller-relevant information content.

To the extent that the transformation function G is generated in thesecond access domain AC2 in the exemplary embodiment elucidated by FIG.1A, the transformation function G is known to the model vendor andshould, for the purposes of keeping the first operating data BD1 to betransformed confidential from the model vendor MA, in particular carryout an information reduction such that the model vendor MA cannotreadily reconstruct the original first operating data BD1.

The trained transformation function G is transmitted from the modelgenerator MG to the controller CTL, i.e., from the second access domainAC2 into the first access domain AC1. In the controller CTL, thetransformation function G is implemented by a trained neural networkNN(G).

The trainable or trained control model H serves to simulate or analyze aphysical, control-theory-related, stochastic and/or other causalrelationship of the technical system TS or a part thereof for thepurposes of predicting, classifying operating data and/or forcontrolling the technical system TS. Hence, the control model H can beused, e.g., for controlling turbines, as a soft sensor, for classifyingtumors based on x-ray images or for predicting weather. The controlmodel H models the technical system TS or a part thereof and/or atechnical or biological structure, depending on which the technicalsystem TS is controlled or influenced. The control model H can beconsidered to be a function or routine which is fed operating data ofthe technical system TS that are transformed by the transformationfunction G as input data and which outputs the control data as outputdata. Here, in particular, the control data can be a result of asimulation, prediction, analysis and/or classification. The controlmodel H should be trained in such a way that control data that areoptimized from the input data in respect of predetermined criteria canbe derived by the control model H. A multiplicity of standard trainingmethods is available for training purposes. By way of example, thepredetermined criteria can be represented here by a suitable costfunction, for the minimization of which a known learning method isimplemented, such as, e.g., supervised, unsupervised and/orreinforcement learning. The control model H is encoded by a datastructure which is decodable by the interpreter INT and which isimplementable in an application-specific manner. In particular, thecontrol model H can comprise a neural network, a data-driven regressor,a support vector machine, a decision tree and/or another analyticalmodel or a combination thereof.

Since a training success, in particular a training success of a neuralnetwork, is not substantially impaired by a preceding transformation ofthe input data, in this case the first operating data BD1, into anon-user-interpretable form in many cases, the control model H, as arule, can also be trained on the basis of transformed operating data forderiving well optimized control data.

The transformation function G and the control model H are implemented byan artificial neural network NN(G, H) in the model generator MG. Thetrained control model H is transmitted from the model generator MG tothe model execution system MES. There, the control model H isimplemented by a neural network NN(H). Here, the control model H remainsoutside the first access domain AC1, i.e., the operator BTS of thetechnical system TS has no access to the control model H.

FIG. 1B specifically elucidates a generation or training of a controlmodel H for the case where the operator BTS of the technical system TSis not prepared to make operating data of the technical system TSavailable to the model vendor MA.

In this case, the transformation function G is generated and trained bythe controller CTL on the basis of the first operating data BD1 in thefirst access domain AC1. The transformation function G is implemented bya neural network NN(G) in the controller CTL. By way of the trainedneural network NN(G), the first operating data BD1 are transformed intotransformed first operating data TBD1 within the first access domainAC1, i.e., outside of the second access domain AC2.

To the extent that the transformation function G in the exemplaryembodiment described by FIG. 1B is generated in the first access domainAC1, the transformation function G is generally not known to the modelvendor MA. So that the transformation by the transformation function Gremoves as little data relevant to the optimized control as possiblefrom the first operating data BD1, a transformation function G thatsubstantially maintains information and/or that is invertible isgenerated, said transformation function carrying out an efficientobfuscation and, optionally, a dimensional reduction of the firstoperating data BD1. As an alternative or in addition thereto, thetransformation function G may comprise application-specificpre-processing of the first operating data BD1 and/or a neuralautoencoder, which optionally carries out a dimensional reduction of thefirst operating data BD1. Moreover, different basis functions can becalculated and used for training the transformation function G or thecontrol model H. Furthermore, additional data can be added to the firstoperating data BD1 in order to ensure or improve a training success forthe transformation function G and/or for the control model H.

The transformed first operating data TBD1 is transmitted from thecontroller CTL to the model generator MG. Thereupon, the model generatorMG generates and trains the control model H based on the transformedfirst operating data TBD1. Here, the control model H is implemented by aneural network NN(H). Otherwise, the transformation function G and thecontrol model H can be used, as described in conjunction with FIG. 1A.

The trained control model H is transmitted from the model generator MGto the model execution system MES. The control model H is implemented bya neural network NN(H), in the model execution system MES. Here, thecontrol model H remains outside of the first access domain AC1, and sothe operator BTS of the technical system TS gains no access to thecontrol model H.

In both exemplary embodiments described in FIGS. 1A and 1B, the neuralnetwork NN(G) can be implemented by means of a neural autoencoder. Anautoencoder comprises an artificial neural network for learningefficient data codes in particular for efficient data compression and/orfor extracting essential or characteristic features of input data.

Such an autoencoder is schematically illustrated in FIG. 2 . In thepresent exemplary embodiments, the neural autoencoder comprises a neuralinput layer IN, at least one hidden layer VS, which has significantlyfewer neurons than the input layer IN, and an output layer OUT, theneurons of which do not correspond to the neurons of the input layer IN.Input data X are fed to the input layer IN, said input data beingpropagated via the hidden layer VS to the output layer OUT. The latteroutputs the propagated data as output data X′. Thereupon, theautoencoder is trained so that the output data X′ deviates as little aspossible from the input data X, for example by virtue of an absolutevalue of a difference X-X′ being minimized.

The input data X are subject to a transformation T during thepropagation from the input layer IN to the hidden layer VS. If a smalldeviation |X-X′| can be achieved by the training, this means that thetransformation T during the propagation of the data from the hiddenlayer VS to the output layer OUT is at least approximately undone, i.e.,the data are subjected approximately to the transformation during thistransition. Furthermore, a small deviation |X-X′| means that the inputdata can already be represented well by the fewer number of neurons ofthe hidden layer VS or can be reconstructed therefrom by means of thetrained layers VS and OUT.

The data propagated by the hidden layer VS thus represent an efficientencoding of the input data X and can be output as transformed input dataZ. On the other hand, a reconstruction of the original input data X fromthe transformed output data Z is only possible with the greatdifficulties without knowledge of the trained hidden layer VS and thetrained output layer OUT. Therefore, an autoencoder is a particularlyadvantageous implementation of the transformation function G within themeaning of embodiments of the invention.

In the present exemplary embodiments, a neural autoencoder is trained asa transformation function G with the first operating data BD1 as inputdata X. The trained autoencoder, i.e., the trained transformationfunction G, outputs the transformed first operating data TBD1 astransformed data Z.

As an alternative or in addition thereto, the transformation function Gmay comprise multiplication by an invertible or non-invertible randommatrix.

FIG. 3 elucidates an implementation of a trained control model, of thetrained control model H in this case, for controlling the technicalmodel TS. Here, the control model H may have been generated and trainedaccording to one of the aforementioned exemplary embodiments. Otherwise,the same entities in FIGS. 1A, 1B and 3 are denoted by the samereference signs.

For the purposes of controlling the technical system TS, secondoperating data BD2 of the technical system TS are captured by thecontroller CTL within the first access domain AC1 and said secondoperating data are transformed by the trained neural network NN(G) toform transformed second operating data TBD2. In particular, this isimplemented outside of the second access domain AC2, and so the modelvendor MA has no access to the second operating data BT2 or to thetransformation function G.

The transformed second operating data TBD2 are transmitted from thecontroller CTL to the model execution system MES. The trained neuralnetwork NN(H) is implemented by the model execution system MES in thesecond access domain AC2 by means of the interpreter INT. Here, thetransformed second operating data TBD2, from which control data CD arederived by the trained control model H, are fed to the trained controlmodel H. In particular, this is implemented outside of the first accessdomain AC1, and so the operator of the technical system TS has no dataaccess to the control model H. The derived control data CD serve tocontrol the technical system TS. In particular, the control data CD maybe simulation data, prediction data, analysis data, state data,classification data, monitoring data and/or other data contributing tothe control of the technical system TS. The control data CD aretransmitted from the model execution system MES to the controller CTL.Then, the controller CTL controls the technical system TS by means ofthe control data CD.

As a result of separating the transformation of the second operatingdata BD2 from the execution of the control model H, it is possible, onthe one hand, for the model vendor MA to keep their control model Hconfidential from the operator BTS and, on the other hand, for theoperator BTS to keep their operating data BD2 confidential from themodel vendor MA. Encrypting the control model H is not necessary in thiscase.

FIG. 4 elucidates a particularly advantageous generation of atransformation function G and a control model H from an initial model F.Here, the initial model F is trained as a whole and the trained initialmodel F is split into the transformation function G and the controlmodel H. Training and splitting is carried out by the model vendor MA,i.e., within the second access domain AC2 and outside of the firstaccess domain AC1. In particular, the split can be set by the modelvendor MA in this case. The split is undertaken in such a way thatconfidential implementation details of the initial model F are encodedas completely as possible in the control model H and not in thetransformation function G. The transformation function G and the controlmodel H which are generated in this way can then be used as describedabove.

The initial model F is a neural network with an input layer IN, aplurality of hidden layers VS1, VS2 and an output layer OUT. At leastone of the hidden layers, in this case VS1, comprises fewer neurons thanthe input layer IN. The initial model F is initially trained as auniform neural network on the basis of input data X, the first operatingdata BD1 in this case, such that the output data Y, the control data CDin this case, which are derived from the input data X are optimized inrespect of predetermined criteria. The aforementioned optimizationcriteria can be used as criteria.

Following its training, the initial model F is split into two partialneural networks at a hidden layer, VS1 in this case. The partial networkwith the layer IN as input layer and the layer VS1 as new output layer,illustrated at the bottom in FIG. 4 , represents a first partialfunction in this case. The transformed data Z output in the lowerpartial network by the hidden layer VS1 are obfuscated, and so the inputdata X cannot readily be reconstructed therefrom. Moreover, thetransformed data are also reduced in terms of their dimensions since thehidden layer VS1 has fewer neurons than the input layer IN.Consequently, the first partial function can be advantageously providedand used as a trained transformation function G for transforming theinput data X, i.e., the second operating data BD2 in this case, intotransformed data Z, i.e., into the transformed second operating dataTBD2 in this case, as described above. Hence, Z=G(X) and TBD2=G(BD2)apply.

The partial network illustrated at the top in FIG. 4 with the hiddenlayer VS1 as new input layer and the original output layer OUT as outputlayer represents a second partial function. The upper partial network istrained to convert data Z that were input at the hidden layer VS1 andtransformed by the transformation function G into the output data Y,i.e., into the optimized control data CD in this case. Consequently, thesecond partial function can advantageously be provided and used astrained control model H for deriving the control data CD from thetransformed second operating data TBD2, as described above. Hence,Y=H(Z) and CD=H(TBD2) apply.

As a result of the model generation elucidated in FIG. 4 , it ispossible to provide a trained transformation function G and a trainedcontrol model H in particularly simple fashion. The partial functions Gand H, which were trained together in a certain sense, are consistent ina natural manner and matched particularly well to one another in thiscase.

Although the present invention has been disclosed in the form ofpreferred embodiments and variations thereon, it will be understood thatnumerous additional modifications and variations could be made theretowithout departing from the scope of the invention.

For the sake of clarity, it is to be understood that the use of “a” or“an” throughout this application does not exclude a plurality, and“comprising” does not exclude other steps or elements.

The invention claimed is:
 1. A method for controlling a technical systembased on a control model, said method comprising: generating, by a modelgenerator residing in a second access domain, the control model as afunction of first operating data received from the technical systemresiding in a first access domain that is separated from the secondaccess domain, said control model configured to output control data uponbeing executed using second operating data that was transformed by beingreduced and/or obfuscated; receiving, by a model execution systemresiding in the second access domain from a controller residing in thefirst access domain, the transformed second operating data; executing,by the model execution system using the transformed second operatingdata, the control model, said executing the control model comprisingoutputting the control data; and transmitting, by the model executionsystem to the controller, the control data, wherein the control data isconfigured to control the technical system by the controller, whereinthe model generator, the model execution system and the controller eachcomprise one or more processors for carrying out all method stepsperformed by the model generator, the model execution system, and thecontroller, respectively.
 2. The method as claimed in claim 1, whereinthe control model is generated by the model generator based on the firstoperating data having been transformed by a transformation function thatreduces and/or obfuscates the first operating data after thetransformation was generated and trained by the model generator based onthe first operating data.
 3. The method as claimed in claim 2, whereinthe model generator and/or the model execution system are operated by amodel vendor in the second access domain, and wherein the transformationfunction is provided to the model generator by the model vendor.
 4. Themethod as claimed in claim 2, wherein the transformation functioncomprises a neural autoencoder.
 5. The method as claimed in claim 2,wherein the transformation function comprises multiplication by a randommatrix.
 6. The method as claimed in claim 1, wherein the control modelcomprises at least one of a neural network, a data-driven regressor, asupport vector machine, and a decision tree.
 7. The method as claimed inclaim 1, wherein an operator (BTS) in the first access domain usesand/or controls the technical system and the controller, and wherein atransformation function that reduces and/or obfuscates the firstoperating data is provided to the controller by the operator (BTS). 8.The method as claimed in claim 1, said method further comprising:training, in the second access domain using the first operating data, aninitial model; and splitting, in the second access domain, the initialmodel into a first partial function and a second partial function,wherein the first partial function is a transformation functionconfigured to reduce and/or obfuscate the first operating data andwherein the second partial function is the control model.
 9. The methodas claimed in claim 1, wherein the control model is trained on the basisof data that are independent of the technical system.
 10. The method asclaimed in claim 1, wherein the control model is generated by the modelgenerator based on the first operating data having been previouslytransformed in the first access domain by a transformation function thatreduces and/or obfuscates the first operating data after thetransformation was generated and trained by the controller based on thefirst operating data.
 11. A computer system comprising one or moreprocessors, one or more memories, a computer readable hardware storagedevice having computer readable program code stored therein, saidprogram code executable by the one or more processors via the one ormore memories to implement a method for controlling a technical systembased on a control model, said method comprising: generating, by a modelgenerator residing in a second access domain, the control model as afunction of first operating data received from the technical systemresiding in a first access domain that is separated from the secondaccess domain, said control model configured to output control data uponbeing executed using second operating data that was transformed by beingreduced and/or obfuscated; receiving, by a model execution systemresiding in the second access domain from a controller residing in thefirst access domain, the transformed second operating data; executing,by the model execution system using the transformed second operatingdata, the control model, said executing the control model comprisingoutputting the control data; and transmitting, by the model executionsystem to the controller, the control data, wherein the control data isconfigured to control the technical system by the controller, whereinthe model generator, the model execution system and the controller eachcomprise one or more processors for carrying out all method stepsperformed by the model generator, the model execution system, and thecontroller, respectively.
 12. The computer system as claimed in claim11, wherein the control model is generated by the model generator basedon the first operating data having been transformed by a transformationfunction that reduces and/or obfuscates the first operating data afterthe transformation was generated and trained by the model generatorbased on the first operating data.
 13. The computer system as claimed inclaim 11, wherein the control model is generated by the model generatorbased on the first operating data having been previously transformed inthe first access domain by a transformation function that reduces and/orobfuscates the first operating data after the transformation wasgenerated and trained by the controller based on the first operatingdata.
 14. A computer program product, comprising a computer readablehardware storage device having computer readable program code storedtherein, said program code executable by one or more processors of acomputer system to implement a method for controlling a technical systembased on a control model, said method comprising: generating, by a modelgenerator residing in a second access domain, the control model as afunction of first operating data received from the technical systemresiding in a first access domain that is separated from the secondaccess domain, said control model configured to output control data uponbeing executed using second operating data that was transformed by beingreduced and/or obfuscated; receiving, by a model execution systemresiding in the second access domain from a controller residing in thefirst access domain, the transformed second operating data; executing,by the model execution system using the transformed second operatingdata, the control model, said executing the control model comprisingoutputting the control data; and transmitting, by the model executionsystem to the controller, the control data, wherein the control data isconfigured to control the technical system by the controller, whereinthe model generator, the model execution system and the controller eachcomprise one or more processors for carrying out all method stepsperformed by the model generator, the model execution system, and thecontroller, respectively.
 15. The computer program product as claimed inclaim 14, wherein the control model is generated by the model generatorbased on the first operating data having been transformed by atransformation function that reduces and/or obfuscates the firstoperating data after the transformation was generated and trained by themodel generator based on the first operating data.
 16. The computerprogram product as claimed in claim 14, wherein the control model isgenerated by the model generator based on the first operating datahaving been previously transformed in the first access domain by atransformation function that reduces and/or obfuscates the firstoperating data after the transformation was generated and trained by thecontroller based on the first operating data.